EUAdb: A resource for COVID-19 test development and comparison.

Due to the sheer number of COVID-19 (coronavirus disease 2019) cases there is a need for increased world-wide SARS-CoV-2 testing capability that is both efficient and effective. Having open and easy access to detailed information about these tests, their sensitivity, the types of samples they use, etc. would be highly useful to ensure their reproducibility, to help clients compare and decide which tests would be best suited for their applications, and to avoid costs of reinventing similar or identical tests. Additionally, this resource would provide a means of comparing the many innovative diagnostic tools that are currently being developed in order to provide a foundation of technologies and methods for the rapid development and deployment of tests for future emerging diseases. Such a resource might thus help to avert the delays in testing and screening that was observed in the early stages of the pandemic and plausibly led to more COVID-19-related deaths than necessary. We aim to address these needs via a relational database containing standardized ontology and curated data about COVID-19 diagnostic tests that have been granted Emergency Use Authorizations (EUAs) by the FDA (US Food and Drug Administration). Simple queries of this actively growing database demonstrate considerable variation among these tests with respect to sensitivity (limits of detection, LoD), controls and targets used, criteria used for calling results, sample types, reagents and instruments, and quality and amount of information provided.

The SARS-CoV-2 pandemic: the race to trace: contact tracing scale-up in San Francisco-early lessons learned.

In order to effectively control spread of coronavirus 2019 (COVID-19), it is essential that jurisdictions have the capacity to rapidly trace close contacts of each and every case. Best practice guidance on how to implement such programs is urgently needed. We describe the early experience in the City and County of San Francisco (CCSF), where the City's Department of Health expanded contact tracing capability in anticipation of changes in San Francisco's 'shelter in place' order between April and June 2020. Important prerequisites to successful scale-up included a rapid expansion of the COVID-19 response workforce, expansion of testing capability, and other containment resources. San Francisco's scale-up offers a model for how other jurisdictions can rapidly mobilize a workforce. We underscore the importance of an efficient digital case management system, effective training, and expansion of supportive service programs for those in quarantine or isolation, and metrics to ensure continuous performance improvement.

Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research.

OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research.

Reporting and design of randomized controlled trials for COVID-19: A systematic review.

BACKGROUND: The novel coronavirus 2019 (COVID-19) pandemic has mobilized global research at an unprecedented scale. While challenges associated with the COVID-19 trial landscape have been discussed previously, no comprehensive reviews have been conducted to assess the reporting, design, and data sharing practices of randomized controlled trials (RCTs). PURPOSE: The purpose of this review was to gain insight into the current landscape of reporting, methodological design, and data sharing practices for COVID-19 RCTs. DATA SOURCES: We conducted three searches to identify registered clinical trials, peer-reviewed publications, and pre-print publications. STUDY SELECTION: After screening eight major trial registries and 7844 records, we identified 178 registered trials and 38 publications describing 35 trials, including 25 peer-reviewed publications and 13 pre-prints. DATA EXTRACTION: Trial ID, registry, location, population, intervention, control, study design, recruitment target, actual recruitment, outcomes, data sharing statement, and time of data sharing were extracted. DATA SYNTHESIS: Of 178 registered trials, 112 (62.92%) were in hospital settings, median planned recruitment was 100 participants (IQR: 60, 168), and the majority (n = 166, 93.26%) did not report results in their respective registries. Of 35 published trials, 31 (88.57%) were in hospital settings, median actual recruitment was 86 participants (IQR: 55.5, 218), 10 (28.57%) did not reach recruitment targets, and 27 trials (77.14%) reported plans to share data. CONCLUSIONS: The findings of our study highlight limitations in the design and reporting practices of COVID-19 RCTs and provide guidance towards more efficient reporting of trial results, greater diversity in patient settings, and more robust data sharing.

Timely access to trial data in the context of a pandemic: the time is now.

OBJECTIVE: Clinical trial data sharing has the potential to accelerate scientific progress, answer new lines of scientific inquiry, support reproducibility and prevent redundancy. Vivli, a non-profit organisation, operates a global platform for sharing of individual participant-level trial data and associated documents. Sharing of these data collected from each trial participant enables combining of these data to drive new scientific insights or assess reproducibility-not possible with the aggregate or summary data tables historically made available. We report on our initial experience including key metrics, lessons learned and how we see our role in the data sharing ecosystem. We also describe how Vivli is addressing the needs of the COVID-19 challenge through a new dedicated portal that provides a direct search function for COVID-19 studies, availability for fast-tracked request review and data sharing. DATA SUMMARY: The Vivli platform was established in 2018 and has partnered with 28 diverse members from industry, academic institutions, government platforms and non-profit foundations. Currently, 5400 trials representing 3.6 million participants are shared on the platform. From July 2018 to September 2020, Vivli received 201 requests. To date, 106 of 201 requests received approval, 5 have been declined, 27 withdrew and 27 are in the revision stage. CONCLUSIONS: The pandemic has only magnified the necessity for data sharing. If most data are shared and in a manner that allows interoperability, then we have hope of moving towards a cohesive scientific understanding more quickly not only for COVID-19 but also for all diseases. Conversely, if only isolated pockets of data are shared then society loses the opportunity to close vital gaps in our understanding of this rapidly evolving epidemic. This current challenge serves to highlight the value of data sharing platforms-critical enablers that help researchers build on prior knowledge.